code: def register_dwi_series(
      data, gtab, affine=None, b0_ref=0, pipeline=None, static_mask=None
  ):
      """Register a DWI series to the mean of the B0 images in that series.
  
      all first registered to the first B0 volume
  
      Parameters
      ----------
      data : 4D array or nibabel Nifti1Image class instance or str
          Diffusion data. Either as a 4D array or as a nifti image object, or as
          a string containing the full path to a nifti file.
  
      gtab : a GradientTable class instance or tuple of strings
          If provided as a tuple of strings, these are assumed to be full paths
          to the bvals and bvecs files (in that order).
  
      affine : 4x4 array, optional.
          Must be provided for `data` provided as an array. If provided together
          with Nifti1Image or str `data`, this input will over-ride the affine
          that is stored in the `data` input. Default: use the affine stored
          in `data`.
  
      b0_ref : int, optional.
          Which b0 volume to use as reference. Default: 0
  
      pipeline : list of callables, optional.
          The transformations to perform in sequence (from left to right):
          Default: ``[center_of_mass, translation, rigid, affine]``
  
      static_mask : array, shape (S, R, C) or (R, C), optional
          static image mask that defines which pixels in the static image
          are used to calculate the mutual information.
  
      Returns
      -------
      xform_img, affine_array: a Nifti1Image containing the registered data and
      using the affine of the original data and a list containing the affine
      transforms associated with each of the
  
      """
      pipeline = pipeline or [
          "center_of_mass",
          "translation",
          "rigid",
          "affine",
      ]
  
      data, affine = read_img_arr_or_path(data, affine=affine)
      if isinstance(gtab, collections.abc.Sequence):
          gtab = dpg.gradient_table(*gtab)
  
      if np.sum(gtab.b0s_mask) > 1:
          # First, register the b0s into one image and average:
          b0_img = nib.Nifti1Image(data[..., gtab.b0s_mask], affine)
          trans_b0, b0_affines = register_series(
              b0_img,
              ref=b0_ref,
              pipeline=pipeline,
              static_mask=static_mask,
          )
          ref_data = np.mean(trans_b0, -1, keepdims=True)
      else:
          # There's only one b0 and we register everything to it
          trans_b0 = ref_data = data[..., gtab.b0s_mask]
          b0_affines = np.eye(4)[..., np.newaxis]
  
      # Construct a series out of the DWI and the registered mean B0:
      moving_data = data[..., ~gtab.b0s_mask]
      series_arr = np.concatenate([ref_data, moving_data], -1)
      series = nib.Nifti1Image(series_arr, affine)
  
      xformed, affines = register_series(
          series, ref=0, pipeline=pipeline, static_mask=static_mask
      )
      # Cut out the part pertaining to that first volume:
      affines = affines[..., 1:]
      xformed = xformed[..., 1:]
      affine_array = np.zeros((4, 4, data.shape[-1]))
      affine_array[..., gtab.b0s_mask] = b0_affines
      affine_array[..., ~gtab.b0s_mask] = affines
  
      data_array = np.zeros(data.shape)
      data_array[..., gtab.b0s_mask] = trans_b0
      data_array[..., ~gtab.b0s_mask] = xformed
  
      return nib.Nifti1Image(data_array, affine), affine_array
  
imports: from warnings import warn
  import re
  import collections.abc
  from functools import partial
  import numbers
  import numpy
  import nibabel
  from dipy.align.metrics import CCMetric, EMMetric, SSDMetric
  from dipy.align.imwarp import SymmetricDiffeomorphicRegistration, DiffeomorphicMap
  from dipy.align.imaffine import transform_centers_of_mass, AffineMap, MutualInformationMetric, AffineRegistration
  from dipy.align.transforms import TranslationTransform3D, RigidTransform3D, RigidScalingTransform3D, RigidIsoScalingTransform3D, AffineTransform3D
  import dipy.core.gradients
  import dipy.data
  from dipy.align.streamlinear import StreamlineLinearRegistration
  from dipy.tracking.streamline import set_number_of_points
  from dipy.tracking.utils import transform_tracking_output
  from dipy.io.streamline import load_trk
  from dipy.io.utils import read_img_arr_or_path
  from dipy.io.image import load_nifti, save_nifti
label_desc: Use image node_id 9 for calling a global function with example usage: register_dwi_series(data, gtab, affine, b0_ref, pipeline, static_mask) and returns: nib,  affine_array